PReLU layer from Caffe

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -422,7 +422,7 @@ CV__DNN_EXPERIMENTAL_NS_BEGIN
     class CV_EXPORTS ChannelsPReLULayer : public ActivationLayer
     {
     public:
-        static Ptr<ChannelsPReLULayer> create(const LayerParams& params);
+        static Ptr<Layer> create(const LayerParams& params);
     };
 
     class CV_EXPORTS ELULayer : public ActivationLayer

modules/dnn/src/caffe/caffe_importer.cpp

@@ -216,7 +216,7 @@ public:
                 shape.push_back((int)_shape.dim(i));
         }
         else
-            CV_Error(Error::StsError, "Unknown shape of input blob");
+            shape.resize(1, 1);  // Is a scalar.
     }
 
     void blobFromProto(const caffe::BlobProto &pbBlob, cv::Mat &dstBlob)
@@ -274,9 +274,9 @@ public:
     struct BlobNote
     {
         BlobNote(const std::string &_name, int _layerId, int _outNum) :
-            name(_name.c_str()), layerId(_layerId), outNum(_outNum) {}
+            name(_name), layerId(_layerId), outNum(_outNum) {}
 
-        const char *name;
+        std::string name;
         int layerId, outNum;
     };
 

modules/dnn/src/init.cpp

@@ -97,6 +97,7 @@ void initializeLayerFactory()
     CV_DNN_REGISTER_LAYER_CLASS(ReLU,           ReLULayer);
     CV_DNN_REGISTER_LAYER_CLASS(ReLU6,          ReLU6Layer);
     CV_DNN_REGISTER_LAYER_CLASS(ChannelsPReLU,  ChannelsPReLULayer);
+    CV_DNN_REGISTER_LAYER_CLASS(PReLU,          ChannelsPReLULayer);
     CV_DNN_REGISTER_LAYER_CLASS(Sigmoid,        SigmoidLayer);
     CV_DNN_REGISTER_LAYER_CLASS(TanH,           TanHLayer);
     CV_DNN_REGISTER_LAYER_CLASS(ELU,            ELULayer);

modules/dnn/src/layers/elementwise_layers.cpp

@@ -754,8 +754,15 @@ Ptr<PowerLayer> PowerLayer::create(const LayerParams& params)
     return l;
 }
 
-Ptr<ChannelsPReLULayer> ChannelsPReLULayer::create(const LayerParams& params)
+Ptr<Layer> ChannelsPReLULayer::create(const LayerParams& params)
 {
+    CV_Assert(params.blobs.size() == 1);
+    if (params.blobs[0].total() == 1)
+    {
+        LayerParams reluParams = params;
+        reluParams.set("negative_slope", params.blobs[0].at<float>(0));
+        return ReLULayer::create(reluParams);
+    }
     Ptr<ChannelsPReLULayer> l(new ElementWiseLayer<ChannelsPReLUFunctor>(ChannelsPReLUFunctor(params.blobs[0])));
     l->setParamsFrom(params);
 

modules/dnn/test/test_layers.cpp

@@ -279,6 +279,11 @@ TEST(Layer_Test_Eltwise, Accuracy)
     testLayerUsingCaffeModels("layer_eltwise");
 }
 
+TEST(Layer_Test_PReLU, Accuracy)
+{
+    testLayerUsingCaffeModels("layer_prelu", DNN_TARGET_CPU, true);
+}
+
 //template<typename XMat>
 //static void test_Layer_Concat()
 //{